Biometrics India, Pfizer Global R & D

The Concept of Randomization and Blinding in Clinical Trials

Suraj P Anand

Randomization

Randomization is the process of assigning clinical trial participants to treatment groups. Randomization gives each participant a known (usually equal) chance of being assigned to any of the groups. Successful randomization requires that group assignment cannot be predicted in advance.

Why Randomize?

If, at the end of a clinical trial, a difference in outcomes occurs between two treatment groups (say, intervention and control) possible explanations for this difference would include:

the intervention exhibits a real effect;

the outcome difference is solely due to chance

there is a systematic difference (or bias) between the groups due to factors other than the intervention.

Randomization aims to obviate the third possibility.

Forms of Randomization

Simple Randomization

Permuted Block Randomization

Stratified Block Randomization

Dynamic (adaptive) random allocation

Simple Randomization

Coin Tossing for each trial participant

Sequence of Random Numbers from statistical textbooks

Computer generated sequence

Illustrations

The computer generated sequence: 4,8,3,2,7,2,6,6,3,4,2,1,6,2,0,…….Two Groups (criterion:even-odd): AABABAAABAABAAA……Three Groups: (criterion:{1,2,3}~A, {4,5,6}~B, {7,8,9}~C; ignore 0’s) BCAACABBABAABA……Two Groups: different randomisation ratios(eg.,2:3):(criterion:{0,1,2,3}~A, {4,5,6,7,8,9}~B) BBAABABBABAABAA……..

Permuted Block Randomization

Used for small studies to maintain reasonably good balance among groups

In a two group design, Blocks having equal numbers of As and Bs (A = intervention and B = control, for example) are used, with the order of treatments within the block being randomly permuted

Illustration

With a block size of 4 for two groups(A,B), there are 6 possible permutations and they can be coded as:

1=AABB, 2=ABAB, 3=ABBA, 4=BAAB, 5=BABA, 6=BBAA

Each number in the random number sequence in turn selects the next block, determining the next four participant allocations (ignoring numbers 0,7,8 and 9).

e.g., The sequence 67126814…. will produce BBAA AABB ABAB BBAA AABB BAAB.

In practice, a block size of four is too small since researchers may crack the code and risk selection bias. Mixing block sizes of between 6 and 12 is better with the size kept unknown to the investigator. This precaution maintains concealment. Simple randomization should determine which block size to use next.

Stratified Block Randomization

Stratified block randomization can further restrict chance imbalances to ensure the treatment groups are as alike as possible for selected prognostic variables or other patient factors. A set of permuted blocks is generated for each combination of prognostic factors

Typical examples of such factors are age group, severity of condition, and treatment centre. Stratification simply means having separate block randomisation schemes for each combination of characteristics (‘stratum’)

For example, in a study where you expect treatment effect to

differ with age and sex you may have four strata: male over 65, male under 65, female over 65 and female under 65

Dynamic (adaptive) random allocation

Simple and block randomization methods are defined, and allocation sequences set up, before the start of the trial.

In contrast, dynamic randomization methods allocate patients to treatment group by checking the allocation of similar patients already randomized, and allocating the next treatment group "live" to best balance the treatment groups across all stratification variables. Biased coin randomization and minimisation are two such methods.

Efron(1971) first introduced the idea of biased coin randomization as a method for adjustment of assigning probabilities. The assigning probability for the first patient is1/2. After k patients are enrolled, with k(A) and k(B)

patients randomized in groups A and B respectively, the idea involves randomizing the next patient to group B with probability greater than ½ if more patients have been randomized to group A at this stage, and vice-versa. If balance is achieved, the next patient is randomized to either of the groups with probability ½.

Example of randomization using the minimisation method in a trial of chemotherapy for breast cancer, with stratification factors of clinic site,

estrogen receptor status (ER+ or ER–) and menopausal status

Characteristic Treatment A Treatment B

Site 1 7 8

Site 2 10 9

ER+ 5 6

ER– 12 11

Premenopausal 8 9

Postmenopausal 9 8

Total 17 17

The next participant (no. 35) is from Site 2, ER+, postmenopausal. Subtotals for treatment allocation to this profile of characteristics are 10 + 5 + 9 = 24 for Treatment A and 9 + 6 + 8 = 23 for Treatment B (note subjects are counted more than once). Participant no. 35 would therefore be allocated to Treatment B. When the tallies on A and B are equal within a profile, the next participant is randomly allocated. This process is equivalent to a permuted block size of two within the profile.

Inappropriate randomisation methods

Assigning patients alternately to treatment group is not random assignment

Assigning the first half of the population to one group is not random assignment

Assignments by methods based on patient characteristics such as date of birth, order of entry into the clinic or day of clinic attendance, are not reliably random

Allocation Concealment It is very important that those responsible for recruiting people into a trial are

unaware of the group to which a participant will be allocated, should that subject agree to be in the study. This avoids both conscious and unconscious selection of patients into the study.For multicentre clinical trials, central randomization by telephone, interactive voice response system, fax or the Internet are ideal methods for allocation concealment. The clinician or data manager at the participating site assesses eligibility, gains consent, and makes the decision to enroll a patient, then calls the randomization service to get the treatment allocation.For single-centre clinical trials, it is usually possible to identify a staff member not involved with the trial who can keep the randomization list or envelopes. They should be instructed to keep the list private, and to only reveal a treatment allocation after receiving information demonstrating that the patient is eligible and has consented to the trial.In situations where remote randomization may not be feasible or desirable, a set of tamper-evident envelopes may be provided to each participating site. The envelopes should look identical, and each should have the trial identification and a sequential number on it. Inside is the treatment allocation and usually a trial identifier for the patient (e.g., unique sequential number). After assessing eligibility and consent, the next envelope in sequence is opened. Care needs to be taken that the envelopes are opaque and well sealed, and that the sequence of opening the envelopes is monitored regularly.

Issues leading to Blinding

Most investigators have firm views about which of a range of alternative treatments is more effective and often, which is more appropriate for particular groups of patients. As a result, there is a strong temptation by investigators to channel particular groups of patients to particular treatments (channeling effect )

There is also a risk of the investigators subconsciously losing their objectivity in their assessments of treatment effects simply because of their clear preference for particular treatments

There is a risk of having other forms of bias, which can be satisfactorily controlled by proper blinding

Bias

Bias is said to have occurred if the results observed reflect other factors in addition to (or even instead of) the effect of the treatment:

Some potential sources of bias:

Patient bias

Care Provider bias

Assessor bias

Laboratory bias

Analysis and Interpretation bias

Patient Bias

the patient's knowledge that the patient is receiving a "new" treatment may substantially affect the patient's subjective assessment

there is a subject x disease interaction in at least some diseases (and virtually all diseases)

thus, the patient's knowledge of the treatment being received may affect the outcome of the study

 

Care Provider Bias

the care provider's knowledge of which treatment a patient is receiving may affect the way the provider

– deals with the patient

– treats the patient

these differences may give the patient information (even if incorrect) about the treatment the patient is receiving, which then may affect the outcome of the study

Assessor Bias

the assessor's knowledge of which treatment the patient is receiving may affect the way the assessor assesses outcome

such a bias would directly affect the validity of the conclusions of the study

if the assessment is done while the patient is still receiving treatment, this may provide the patient with information about the treatment being received

Laboratory Bias

the knowledge of which treatment the patient received may affect the way in which the test is run or interpreted, or be retested.

although this is most severe with subjectively graded results (pathology slides, photographs, ECG, etc.), this can also be a problem with "objective tests" such as laboratory assays which may be run subtly differently by the technician.

Analysis and Interpretation bias

knowledge of the treatment group may affect the results of the analysis of the data by

– seeking an explanation of an "anomalous” finding when one is found contrary to the study hypothesis

– accepting a "positive" finding without fully exploring the data

knowledge of the treatment group may affect the decisions made by external monitors of a study by

– terminating a study for adverse events because they fit the expectations of the monitors

– terminating a study for superiority of treatment because it fits the expectations of the monitors

Blinding

All of these potential problems can be avoided if everyone involved in the study is blinded to the actual treatment the patient is receiving.

Blinding (also called masking or concealment of treatment) is intended to avoid bias caused by subjective judgment in reporting, evaluation, data processing, and analysis due to knowledge of treatment.

Hierarchy of Blinding

open label: no blinding

single blind: patient (usually;occasionally may be assessor) blinded to treatment

double blind: patient and assessors (who often are also the health care providers and data collectors) blinded to treatment

complete blind: everyone involved in the study blinded to treatment

Open Label Studies

These may be useful for • pilot studies • dose ranging studies

However, even these applications may be substantially biased by knowledge of the treatment given and may result in

• toxicity over (or under) reported • efficacy over estimated

Even a small fraction of patients assigned at random to placebo will reduce these potential problems substantially.

Single Blind Studies

single blind studies are usually done to blind the patient to the treatment given. Health care providers and assessors

usually know the actual treatment given

justification is usually that double-blind is "impractical" because of need to adjust medication, medication affecting laboratory values, potential side effects, etc.

a single blind study should be used only when it would be unacceptable ethically to give an appropriate placebo

treatment to a patient, and in such a case, the assessor (not the patient) should be the one blinded to the treatment

Double Blind Studies

When both the subjects and the investigators are kept from knowing who is assigned to which treatment, the experiment is called “double blind"

Serve as a standard by which all studies are judged, since it minimizes both potential patient biases and potential assessor biases

Should be used whenever possible, which is whenever it is ethically

permissible to blind a patient

Double Blinding:Techniques

Coded treatment groupsPlacebo for each possible treatment

- tablets identical in physical appearance - tablets with similar taste and smell - IV infusions would normally be the same carrier as

used for active medications

Other treatments "shammed" as far as possible: – minimal power ultrasound therapy when testing

effect of physical therapy in back pain – breathing exercises when assessing the effect of

conditioning exercises

Double Blinding:always feasible??

Situations when double blinding might not be possible

it might not be ethically permissible to blind a patient. As an example, it is unlikely that sham surgery would be considered ethical in a study

it might not be possible to blind a patient. For example, it would be hard to blind a patient to the therapy given in an exercise study

it might not be possible to blind a patient while comparing utility of different invasive procedures

Double Blind Studies:stumbling blocks

Side effects:

side effects (observable by patient) are much harder to blindin general, there are significant ethical problems using placebos to induce side effects in patientsside effects are, in fact, one of the major ways in which blinding is brokena way to avoid it is that the side effects of all the potential therapies be combined into a single list, so that knowledge of side effects would not indicate therapy (at least to patient)

Efficacy:a truly effective treatment can be recognized by its efficacy in patientsalthough rare, some new treatments truly are major leaps. when this happens, it is usually very clear which treatment a patient is receiving, at least for the health care providers involved in the trial

Complete Blinding

probably the best approach which can be used, but requires two groups for data processing, one group to encode the

data/analysis and one group to perform the analysis

normally only available in major drug company studies, and not routinely used even then

Complete Blinding:Techniques

analysis uses coded treatment groups analysis uses coded side effects (e.g., side effects coded using non-standard scheme, with only numeric codes available at time of analysis)analysis uses coded laboratory tests (e.g., name of test coded numerically at time of analysis, using non-standard code)